Wind turbine generator independent variable pitch control method embedded with wind speed self-adaptive predictive control

Abstract

The invention discloses a wind turbine generator independent variable pitch control method embedded with wind speed self-adaptive predictive control, which belongs to the field of wind power, and comprises the following steps: establishing a mathematical model of a large wind turbine generator independent variable pitch system; establishing a mathematical model of a self-adaptive model prediction controller; establishing a mathematical model of a linear processor; and based on the established adaptive model prediction controller, taking a pitch angle signal as the input of a variable pitch system, and carrying out independent variable pitch control on the wind turbine generator. The linear processor is used for limiting the upper and lower output bounds of the signals, the variable pitch signals are dispersed into a plurality of signals to be output in one communication period, and the frequency transformation and amplitude fluctuation of the signals are reduced, so that the fatigue loss of a driving mechanism is reduced, the blade load reduction effect is improved, and the real-time change characteristic of the system is coped with through the wind speed self-adaption criterion. The problem of model mismatching caused by wind speed changes is solved, the performance of the controller is improved, and the method is suitable for systems with high control performance requirements and parameter changes.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wind power, in particular to a wind turbine independent variable pitch control method embedded with wind speed adaptive prediction control. BACKGROUND

[0002] The increase of wind turbine power generation makes the structure of wind turbine tend to be large-scale, the flexibility of blades and tower, and the influence of wind shear and tower shadow effect become significant. Through the research of blade variable pitch control technology, the influence can be reduced by reducing the size and fluctuation of aerodynamic load, and the safe and stable operation of wind turbine can be ensured.

[0003] Blade variable pitch control technology is divided into two types: overall variable pitch and independent variable pitch. Overall variable pitch technology is a control strategy based on given power to calculate the variable pitch angle, and then the pitch angle signal is transmitted to the blade, and the three blades perform unified variable pitch action. Generally, PI or PID controller is used. The advantage of overall variable pitch technology is large adjustment range and simple structure, but the blade unbalanced load is large, especially in large wind turbine and turbulent wind conditions. Independent variable pitch technology transmits different pitch angle signals to the corresponding blades, and the three blades adjust the pitch angle independently according to the respective control signals. The advantage of independent variable pitch technology is that it can well compensate the influence of aerodynamic imbalance factors and effectively reduce the load of blades in the pitch and flap direction. However, the single variable pitch drive and high frequency output will cause fatigue damage. Traditional independent variable pitch is achieved by designing different single-input single-output controllers, or using optimized parameters and different control methods. However, based on the characteristics of nonlinearity, time-varying and strong coupling of wind turbine, it cannot be well satisfied, therefore, it is necessary to use multiple-input multiple-output controller. Model predictive control is such a control method, which has obvious advantages compared with traditional control methods.

[0004] The current model predictive control in the field of wind power variable pitch control is roughly divided into two categories, one is a basic model predictive controller, and the other is an advanced model predictive controller. The control technology based on the basic model predictive controller has been proved to have good effects in the speed control, reduction of blade load and fatigue loss, and suppression of power generation power fluctuation, but the results of the model predictive control are very dependent on the accuracy of modeling of the controlled object. The advanced model predictive controller includes a variable gain model predictive controller, a nonlinear model predictive controller and an adaptive model predictive controller, etc., which better adapts to the problem of model accuracy change caused by real-time change of the wind turbine, and has better effects in speed control and load reduction. In terms of blade load reduction, in addition to considering the problem of model parameter change caused by system change, the processing of the variable pitch signal is also very important, because the amplitude fluctuation and speed change of the variable pitch signal and the output frequency will directly affect the load fluctuation of the blade and the working strength of the drive structure. Therefore, ignoring the model mismatch problem caused by the parameter change of the system real-time change leading to the model accuracy change and the processing of the variable pitch signal will certainly affect the control effect of the controller, but the existing advanced model predictive controller mostly does not process the variable pitch signal or does not consider the model mismatch problem caused by the real-time change of the system. SUMMARY

[0005] In order to solve the above technical problems, the present application provides a wind turbine independent variable pitch control method embedded with wind speed adaptive predictive control, which has simple algorithm and good control performance.

[0006] The technical scheme for solving the above technical problems is: a wind turbine independent variable pitch control method embedded with wind speed adaptive predictive control, comprising the following steps:

[0007] Step S1, establishing a mathematical model of a large wind turbine independent variable pitch system: linearizing the aerodynamic load nonlinear model near the steady state operating point to obtain a linear time-varying model in the rotating coordinate system, and then obtaining a linear time-invariant model in the fixed coordinate system and a state space equation through the Kormann transformation;

[0008] The specific steps of the step S1 are:

[0009] Step S11: establishing a linear time-varying model of the aerodynamic load of a large wind turbine in the rotating coordinate system:

[0010] The motion equation of the wind wheel is expressed as:

[0011]

[0012] The tower motion equation is expressed as:

[0013]

[0014] Step S2, establishing a wind speed adaptive predictive control model: The blade root bending moment output equation is expressed as:

[0015]

[0016] wherein, is the sum of the rotor inertia of the wind wheel and the engine, is the rotational speed of the wind wheel, is the derivative of , is the blade pitch displacement, is the blade pitch movement speed, is the pitch movement acceleration, is the effective wind speed on the first blade, , , is the pitch angle of the first blade, is the generator torque, is the tower top mass, is the wind wheel radius, is the tower height, is the tower top damping coefficient, is the tower top stiffness coefficient, is the tower top damping coefficient, is the tower top stiffness coefficient, is the blade root bending moment of the first blade, is the aerodynamic load coefficient linearized at the operating point;

[0017] Step S12: converting the linear time-varying model in the rotating coordinate system into a linear time-invariant model in the fixed coordinate system by using the Kormann transformation;

[0018] Step S2, establishing a mathematical model of the adaptive model predictive controller: on the basis of the state space equation obtained in step S1, establishing a complete adaptive model predictive controller;

[0019] Step S3, establishing a mathematical model of the linear processor: the control of the variable pitch speed corresponds to the variable pitch speed trapezoidal curve, by integrating the variable pitch speed trapezoidal curve in different time periods within the communication period, limiting the maximum and minimum values of the variable pitch speed, completing the linearization and discretization processing of the variable pitch signal, and obtaining multiple discontinuous pitch angle signals;

[0020] Step S4, based on the established adaptive model predictive controller, taking the pitch angle signal obtained in step S3 as the input of the variable pitch system, and performing independent variable pitch control of the wind turbine.

[0021] The above-mentioned wind turbine independent variable pitch control method embedded with the wind speed adaptive prediction control, in step S11, The specific calculation is as follows:

[0022]

[0023] are the first The force and moment in the flapping direction of the blade root, are the first The effective wind speed in the flapping direction of the blade.

[0024] The wind turbine independent variable pitch control method embedded with the wind speed adaptive predictive control, the specific steps of the step S12 are:

[0025] Step S121: the Koereman transformation matrix As follows, where t represents time:

[0026]

[0027] The inverse matrix of the Koereman transformation is:

[0028]

[0029] Step S122: obtain the linear time-invariant model in the fixed coordinate system through the Koereman inverse transformation, and the specific steps are as follows:

[0030]

[0031] wherein, , , the superscript represents transposition, the superscript represents the variable after Koereman transformation, and the subscript represents different blades, and are the pitch moment and yaw moment, respectively;

[0032] Step S123: let the state variable , the control variable , the output variable , and the wind speed be regarded as a disturbance, and the state space equation is obtained from each formula in step S122:

[0033]

[0034] wherein, is the derivative of , is the coefficient matrix.

[0035] In the step S123 of the wind turbine independent variable pitch control method embedded with the wind speed adaptive predictive control, A, B, C, and D are respectively:

[0036] ;

[0037] ;

[0038] ;

[0039] .

[0040] The above-mentioned wind turbine independent pitch control method with embedded wind speed adaptive predictive control, specifically step S2, includes the following steps:

[0041] Step S21: Establish an adaptive model predictive controller. The predictive model is obtained by discretizing the state-space equations as follows:

[0042]

[0043] In the formula, yes The state at any given moment, yes The state at any given moment, It is a control variable exist Time-based control input, yes Output at any moment Yes After performing discrete operations, the corresponding coefficient matrix is ​​used to predict the output. The predicted output matrix Y is then written in matrix form as follows:

[0044]

[0045] Among them, F and Both are coefficient matrices;

[0046] Step S22: Define the cost function;

[0047] The purpose of a predictive model is to optimize the cost function, that is, to achieve the optimal solution between the actual system output, the error of the reference value, and the system input; the cost function The expression is as follows:

[0048]

[0049] In the formula, the first term The second term represents the error between the actual system output and the reference value. Indicates system input, Indicates the output range. Indicates the control range. It is the output weight matrix. is the input weight matrix, is the reference trajectory at time k, represents the output at time k, predicted at time k, predicted at time k, represents the input at time k, predicted at time k, predicted at time k, subscript , represents the count;

[0050] Step S23: Set the constraint condition of the input, the expression is as follows:

[0051]

[0052] In the formula, represents the system state at time k, represents the initial state of the system, represents the upper and lower limits of the input, represents the upper and lower limits of the input rate of change;

[0053] Step S24: Set the adaptive criterion, take the wind speed as the adaptive reference, update the prediction model, that is, replace with the corresponding coefficient matrix that changes with the wind speed:

[0054]

[0055] In the formula: is the system dynamics model; is the linearization working point of the system; is the partial derivative of the aerodynamic torque with respect to the rotor speed; is the partial derivative of the aerodynamic torque with respect to the wind speed.

[0056] The above-mentioned wind turbine independent variable pitch control method embedded with wind speed adaptive prediction control, in the step S21, F, The expression is as follows:

[0057]

[0058]

[0059] wherein, represents the control time domain, represents the prediction time domain, and .

[0060] The above-mentioned wind turbine independent variable pitch control method embedded with wind speed adaptive prediction control, in the step S3, according to the variable pitch speed curve, the mathematical model of the linear processor is designed as:

[0061]

[0062] wherein, is an output pitch angle, is an initial pitch angle, is a pitch rate, represents a changed pitch angle, , respectively represent an initial pitch rate, a maximum positive acceleration pitch rate and a maximum reverse acceleration pitch rate, and , represents different time points in a communication cycle, and subscript , is a pitch acceleration at different time points.

[0063] The present application has the beneficial effects that:

[0064] 1. According to the characteristics of high-frequency output of the pitch signal, large amplitude change and real-time change of the system in the independent pitch control process of the large wind turbine, the present application proposes an adaptive model predictive controller combined with a linear processor; the upper and lower boundaries of the signal output are limited by the linear processor, the pitch signal is discretized into multiple signal outputs in a communication cycle, the frequency conversion and amplitude fluctuation of the signal are reduced, the fatigue loss of the driving mechanism is reduced, the blade load reduction effect is improved, the characteristics of real-time change of the system are dealt with through the wind speed adaptive criterion, the model mismatch problem caused by the change of the wind speed is solved, the performance of the controller is improved, and the controller is suitable for systems with high control performance requirements and parameter changes.

[0065] 2. The present application well adapts to the nonlinear factors of the wind turbine, considers the characteristics of real-time change of the system, further improves the effect of the model predictive control method in load reduction, and further improves the application range and practicality of the technology, and can still have good load reduction effect under turbulent wind conditions. BRIEF DESCRIPTION OF DRAWINGS

[0066] Figure 1 is a whole flow chart of the present application.

[0067] Figure 2 is a schematic diagram of the linear processor.

[0068] Figure 3 is a schematic diagram of the adaptive model predictive controller.

[0069] Figure 4 is a comparison diagram of the blade root bending moment experimental results of the present application, the single MPC controller and the MPC controller combined with the linear processor under the condition of the average wind speed of 14 m / s.

[0070] Figure 5This is a comparison chart of the pitch moment experimental results of the present invention with those of a standalone MPC controller and an MPC controller with a combined linear processor, under an average wind speed of 14 m / s. Detailed Implementation

[0071] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0072] like Figure 1 As shown, an independent pitch control method for wind turbines with embedded wind speed adaptive predictive control includes the following steps:

[0073] Step S1: Establish a mathematical model of the independent pitch system of a large wind turbine: Linearize the nonlinear model of aerodynamic load near the steady-state operating point to obtain a linear time-varying model in a rotating coordinate system, and then obtain a linear time-invariant model and state-space equations in a fixed coordinate system through the Coleman transformation.

[0074] The specific steps of step S1 are as follows:

[0075] Step S11: Establish a linear time-varying model of the aerodynamic load of a large wind turbine in a rotating coordinate system:

[0076] The equation of motion for the wind turbine is expressed as:

[0077]

[0078] The equation of motion for the tower is expressed as:

[0079]

[0080] No. The equation for the output of the bending moment at the blade root is expressed as:

[0081]

[0082] in, It is the sum of the rotational inertia of the wind turbine and the engine. It is the wind turbine speed. yes The derivative of It is the pitch displacement of the blade. It is the pitching speed of the blade. It is the acceleration of pitch motion. It is the first Effective wind speed on the blades , It is the first The azimuth angle of the blade. It is the generator torque. It is the mass of the top of the tower. It is the radius of the wind turbine. is the tower height, is the first is the pitch angle of the blade, is the top damping coefficient, is the top stiffness coefficient, is the first is the blade root bending moment, is the linearized aerodynamic load coefficient at the operating point;

[0083] The specific calculation is as follows:

[0084]

[0085] is the first is the force and moment in the blade root edgewise direction, is the first is the effective wind speed in the flapwise direction.

[0086] Step S12: convert the linear time-varying model in the rotating coordinate system into a linear time-invariant model in the fixed coordinate system by using the Kormon transformation.

[0087] The specific steps of the step S12 are as follows:

[0088] Step S121: the Kormon transformation matrix is as follows, where t represents time:

[0089]

[0090] The inverse Kormon transformation matrix is as follows:

[0091]

[0092] Step S122: obtain the linear time-invariant model in the fixed coordinate system by Kormon inverse transformation, and the specific steps are as follows:

[0093]

[0094] wherein, , , the superscript represents transposition, the superscript represents the variable after Kormon transformation, and the subscript represents different blades, and are the pitch moment and yaw moment, respectively;

[0095] Step S123: set the state variable , the control variable , and the output variable wind speed The state space equation is obtained from the formulas in step S122, and is regarded as a disturbance.

[0096]

[0097] wherein, is the derivative of , is a coefficient matrix, and the coefficient matrices A, B, C, and D are respectively:

[0098] ;

[0099] ;

[0100] ;

[0101] .

[0102] Step S2: Establishing a mathematical model of the adaptive model predictive controller: on the basis of the state space equation obtained in step S1, a complete adaptive model predictive controller is established.

[0103] As shown in FIG. 2, the step S2 includes the following steps: Figure 3

[0104] Step S21: Establishing an adaptive model predictive controller, and obtaining a prediction model by discretizing the state space equation as follows:

[0105]

[0106] wherein, is the state at time t, is the state at time t, is the control variable at time t, is the control input at time t, is the output at time t, is the corresponding coefficient matrix after discretizing the operation on t, and the prediction output is written in a matrix form to obtain a prediction output matrix Y as follows:

[0107]

[0108] wherein, F and are both coefficient matrices;

[0109] Step S22: Setting a cost function;

[0110] ​​​​The purpose of the prediction model is to make the cost function optimal, i.e. to make the error between the system actual output and the reference value and the system input optimal; the cost function is expressed as follows:

[0111]

[0112] In the formula, the first term represents the error between the system actual output and the reference value, the second term represents the system input, represents the output range, represents the control range, is the output weight matrix, is the input weight matrix, is the reference trajectory at the time t, represents the output at the time t+1 predicted at the time t, represents the input at the time t+1 predicted at the time t, represents the input at the time t+1 predicted at the time t, represents the input at the time t+1 predicted at the time t, represents the index; Step S23: setting the constraint condition of the input, the expression is as follows:

[0113] In the formula,

[0114] represents the system state at the time t, represents the initial state of the system, represents the upper and lower limits of the input,

[0115] represents the upper and lower limits of the input rate of change; Step S24: setting the adaptive criterion, taking the wind speed as the adaptive reference, updating the prediction model, i.e. replacing with the corresponding coefficient matrix that changes with the wind speed:

[0116]

[0117]

[0118] In the formula: is the system dynamics model; is the working point of the system linearization; is the partial derivative of the aerodynamic torque to the wind speed; is the partial derivative of the aerodynamic torque to the wind speed.

[0119] ​​​​Step S3, a mathematical model of the linear processor is established: the control of the pitch rate corresponds to the pitch rate trapezoidal curve, by integrating the pitch rate trapezoidal curve in different time periods in the communication period, the maximum and minimum values of the pitch rate are limited, the linearization and discretization processing of the pitch signal is completed, and a plurality of discontinuous pitch angle signals are obtained.

[0120] As shown in Figure 2 , according to the pitch rate curve, the mathematical model of the linear processor is designed as:

[0121]

[0122] Wherein, is the output pitch angle, is the initial pitch angle, is the pitch rate, represents the changed pitch angle, , respectively represent the initial pitch rate, the maximum positive acceleration pitch rate and the maximum reverse acceleration pitch rate, and , represents different time points in the communication period, and the subscript , is the pitch acceleration at different time points. Through the above formula, the linearization processing of the set pitch angle can be completed. For example, in order to ensure the stability of the power generation, the required pitch rate is , the communication period is , and the pitch instruction received by the pitch controller is:

[0123]

[0124] In the formula, is the pitch angle, is the sending times. Therefore, the pitch instructions received by the pitch controller are in turn, so as to achieve the purpose of reducing the frequency and amplitude fluctuation of the pitch signal.

[0125] Under the condition of turbulent flow, the wind speed changes frequently, the blade pitch angle will frequently switch between the maximum acceleration and deceleration state, and the blade load fluctuation is relatively large. The processed pitch signal can reduce the degree of change, so that the blade load is more stable.

[0126] Step S4, based on the established adaptive model predictive controller, the pitch angle signal obtained in step S3 is taken as the input of the pitch system, and the independent pitch control of the wind turbine is carried out.

[0127] Simulation experiment

[0128] All experiments are carried out on the same computer, and the computer is configured as follows: Intel(R) Core(TM) i7-8700 CPU @ 3.20GHz, RAM 16 GB and GPU of NVIDIA GeForce GTX 1050. In order to verify the effect of the controller, simulation experiments under turbulent wind with an average wind speed of 14 m / s are carried out in MATLAB / Simulink. Important parameters: wind wheel diameter 126 m, hub diameter 3 m, hub height 90 m, wind wheel mass 110 t, cut-in wind speed 4 m / s, average wind speed 14 m / s. In order to verify the load reduction effect, the experimental results of the method of the application and the MPC controller alone and the MPC controller combined with the linear processor are given, and the experimental results are as shown in Figure 4 、 Figure 5 , Figure 4 、 Figure 5 It can be seen from Figure 4 , Figure 5 that the method of the application can achieve good load reduction effect under system parameter change, large frequency change of pitch signal amplitude and turbulent wind condition, and is superior to the MPC control alone and the MPC control method combined with the linear processor, Figure 4 the experimental results obtained by the method in the application are lower than those of other methods.

[0129] In summary, the application considers the linearization processing of the independent pitch signal, updates the prediction model in time with the wind speed as the adaptive criterion, thereby improving the problem that the model predictive controller highly depends on the model precision of the controlled object, and cannot timely adjust the control effect according to the real-time change of the system. The discrete output idea of the linear processor is combined with the characteristics of the adaptive model predictive control to introduce the independent pitch control to update the prediction model in real time, which reduces the fatigue load of the drive structure and the unbalanced load of the blade, prolongs the service life of the wind turbine generator set, can be applied to the system with multiple inputs and multiple outputs, coupling, nonlinearity, time variation and model mismatch caused by real-time change of parameters, and widens the application range.

Claims

1. A wind turbine independent pitch control method with embedded wind speed adaptive predictive control, characterized in that, Includes the following steps: Step S1: Establish a mathematical model of the independent pitch system of a large wind turbine: Linearize the nonlinear model of aerodynamic load near the steady-state operating point to obtain a linear time-varying model in the rotating coordinate system, and then obtain a linear time-invariant model and state-space equations in the fixed coordinate system through the Coleman transformation. The specific steps of step S1 are as follows: Step S11: Establish a linear time-varying model of the aerodynamic load of a large wind turbine in a rotating coordinate system: The equation of motion for the wind turbine is expressed as: ； The equation of motion for the tower is expressed as: ； No. The equation for the output of the bending moment at the blade root is expressed as: ； in, It is the sum of the rotational inertia of the wind turbine and the engine. It is the wind turbine speed. yes The derivative of It is the pitch displacement of the blade. It is the pitching speed of the blade. It is the acceleration of pitch motion. It is the first Effective wind speed on the blades , It is the first The azimuth angle of the blade. It is the generator torque. It is the mass of the top of the tower. It is the radius of the wind turbine. It is the tower height. It is the first Blade pitch angle, It is the damping coefficient at the top of the tower. It is the stiffness coefficient at the top of the tower. It is the first Leaf root bending moment It is the linearized aerodynamic load coefficient at the operating point; Step S12: Use the Coleman transformation to convert the linear time-varying model in the rotating coordinate system into a linear time-invariant model in the fixed coordinate system; Step S2, establish the mathematical model of the adaptive model predictive controller: Based on the state-space equations obtained in step S1, establish the complete adaptive model predictive controller; Step S3: Establish the mathematical model of the linear processor: The control of pitch speed corresponds to the pitch speed trapezoidal curve. By integrating the pitch speed trapezoidal curves of different time periods within the communication cycle, the maximum and minimum values ​​of the pitch speed are limited, and the linearization and discretization of the pitch signal are completed to obtain multiple discontinuous pitch angle signals. Step S4: Based on the established adaptive model predictive controller, the pitch angle signal obtained in step S3 is used as the input of the pitch system to perform independent pitch control of the wind turbine.

2. The wind turbine independent pitch control method with embedded wind speed adaptive predictive control according to claim 1, characterized in that, In step S11 The specific calculations are as follows: ； They are the first The force and torque in the direction of the oscillation at the leaf root. It is the first Effective wind speed in the direction of blade flare.

3. The wind turbine independent pitch control method with embedded wind speed adaptive predictive control according to claim 1, characterized in that, The specific steps of step S12 are as follows: Step S121: Coleman Transformation Matrix As follows, where t represents time: ； Coleman transform inverse matrix for: ； Step S122: Obtain the linear time-invariant model in a fixed coordinate system through the inverse Coleman transform, as follows: ； in, , superscript Indicates transpose, superscript The subscript represents the variable after the Coleman transformation. Indicates different types of blades, and These are the pitching moment and the yaw moment, respectively. Step S123: Set the state variables Control variables Output variables Wind speed Treating it as a disturbance, the state-space equations obtained from the equations in step S122 are as follows: ； in, for The derivative of It is a coefficient matrix.

4. The wind turbine independent pitch control method with embedded wind speed adaptive predictive control according to claim 3, characterized in that, In step S123, A, B, C, and D are respectively: ； ； ； 。 5. The wind turbine independent pitch control method with embedded wind speed adaptive predictive control according to claim 3, characterized in that, The specific steps of step S2 are as follows: Step S21: Establish an adaptive model predictive controller. The predictive model is obtained by discretizing the state-space equations as follows: ； In the formula, yes The state at any given moment, yes The state at any given moment, It is a control variable exist Time-based control input, yes Output at any moment Yes After performing discrete operations, the corresponding coefficient matrix is ​​used to predict the output. The predicted output matrix Y is then written in matrix form as follows: ； Among them, F and Both are coefficient matrices; Step S22: Define the cost function; The purpose of a predictive model is to optimize the cost function, that is, to achieve the optimal solution between the actual system output, the error of the reference value, and the system input; the cost function The expression is as follows: ； In the formula, the first term The second term represents the error between the actual system output and the reference value. Indicates system input, Indicates the output range. Indicates the control range. It is the output weight matrix. It is the input weight matrix. yes Reference trajectory at any moment Indicates in Time prediction in Output at any moment Indicates in Time prediction in Input of time, subscript , Indicates counting; Step S23: Set the input constraints, as shown in the following expression: ； In the formula, This represents the system state at time k. This represents the initial state of the system. Indicates the upper and lower limits of the input. Indicates the upper and lower limits of the input rate of change; Step S24: Set the adaptive criterion, using wind speed as the adaptive reference, and update the prediction model, that is... Replace with the corresponding coefficient matrix that varies with wind speed. : ； In the formula: It is a system dynamics model; It is the operating point where the system is linearized; It is the partial derivative of the aerodynamic torque with respect to the wind turbine speed; It is the partial derivative of aerodynamic torque with respect to wind speed.

6. The wind turbine independent pitch control method with embedded wind speed adaptive predictive control according to claim 5, characterized in that, In step S21, F, The expression is as follows: ； ； in, Indicates control of the time domain, Indicates the prediction time domain, and .

7. The wind turbine independent pitch control method with embedded wind speed adaptive predictive control according to claim 1, characterized in that: In step S3, based on the pitch speed curve, the mathematical model of the linear processor is designed as follows: ； in, To output the propeller pitch angle, The initial pitch angle, It's the pitch speed. Indicates the changing pitch angle, , These represent the initial pitch speed, the maximum forward acceleration pitch speed, and the maximum reverse acceleration pitch speed, respectively. , Indicates different points in time within the communication period, subscript , It refers to the pitch acceleration at different points in time.

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